Single-cell technologies now support two complementary modes of studying gene regulation: population-scale profiling across many individuals, which enables analysis of how gene co-expression varies with biological covariates, and perturbation-based experiments such as Perturb-seq, which enable causal interrogation of regulatory relationships. In this talk, I present statistical methods tailored to each setting. For population-level single-cell data, I develop Fréchet regression on the Bures–Wasserstein manifold to model covariance matrix–valued outcomes and test how subject-specific gene co-expression structure changes with covariates. For Perturb-seq data, I introduce an instrumental-variable framework that leverages genetic perturbations to recover causal gene regulatory graphs while remaining robust to unobserved confounders. Applications include aging-related changes of gene co-expressions in CD4⁺ naïve and central memory T cells from PBMCs and causal network reconstruction from CRISPR interference experiments in K562 cells.
Zoom link: https://uoc-gr.zoom.us/j/88177475079?pwd=ik9vJRdyknP8xneS5NumlqgQdnplaz.1
